{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "你好，Colaboratory",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JiWang-CIT/LearnColab/blob/master/%E4%BD%A0%E5%A5%BD%EF%BC%8CColaboratory.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "9J7p406abzgl"
      },
      "cell_type": "markdown",
      "source": [
        "<img height=\"60px\" src=\"/img/colab_favicon.ico\" align=\"left\" hspace=\"20px\" vspace=\"5px\">\n",
        "\n",
        "## 欢迎使用 Colaboratory！\n",
        "\n",
        "Colaboratory 是免费的 Jupyter 笔记本环境，不需要进行任何设置就可以使用，并且完全在云端运行。要了解更多信息，请参阅我们的[常见问题解答](https://research.google.com/colaboratory/faq.html)。"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "-Rh3-Vt9Nev9"
      },
      "cell_type": "markdown",
      "source": [
        "## 使用入门\n",
        "- [Colaboratory 概览](/notebooks/basic_features_overview.ipynb)\n",
        "- [加载和保存数据：本地文件、云端硬盘、表格、Google Cloud Storage](/notebooks/io.ipynb)\n",
        "- [导入库和安装依赖项](/notebooks/snippets/importing_libraries.ipynb)\n",
        "- [使用 Google Cloud BigQuery](/notebooks/bigquery.ipynb)\n",
        "- [表单](/notebooks/forms.ipynb)、[图表](/notebooks/charts.ipynb)、[Markdown](/notebooks/markdown_guide.ipynb) 以及[微件](/notebooks/widgets.ipynb)\n",
        "- [支持 GPU 的 TensorFlow](/notebooks/gpu.ipynb)\n",
        "- [机器学习速成课程](https://developers.google.com/machine-learning/crash-course/)：[Pandas 简介](/notebooks/mlcc/intro_to_pandas.ipynb)以及[使用 TensorFlow 的起始步骤](/notebooks/mlcc/first_steps_with_tensor_flow.ipynb)\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "1fr51oVCHRZU"
      },
      "cell_type": "markdown",
      "source": [
        "## 重要功能"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "9wi5kfGdhK0R"
      },
      "cell_type": "markdown",
      "source": [
        "### 执行 TensorFlow 代码"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "S9GW-n-oYWIj"
      },
      "cell_type": "markdown",
      "source": [
        "借助 Colaboratory，您只需点击一下鼠标，即可在浏览器中执行 TensorFlow 代码。下面的示例展示了两个矩阵相加的情况。\n",
        "\n",
        "$\\begin{bmatrix}\n",
        "  1. & 1. & 1. \\\\\n",
        "  1. & 1. & 1. \\\\\n",
        "\\end{bmatrix} +\n",
        "\\begin{bmatrix}\n",
        "  1. & 2. & 3. \\\\\n",
        "  4. & 5. & 6. \\\\\n",
        "\\end{bmatrix} =\n",
        "\\begin{bmatrix}\n",
        "  2. & 3. & 4. \\\\\n",
        "  5. & 6. & 7. \\\\\n",
        "\\end{bmatrix}$"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "7UgwxnAA55g3",
        "outputId": "9589151d-5ff4-4bca-f05e-8bcba8ffcba1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "input1 = tf.ones((2, 3))\n",
        "input2 = tf.reshape(tf.range(1, 7, dtype=tf.float32), (2, 3))\n",
        "output = input1 + input2\n",
        "\n",
        "with tf.Session():\n",
        "  result = output.eval()\n",
        "result  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2., 3., 4.],\n",
              "       [5., 6., 7.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "nwYF0E3Sjiy4"
      },
      "cell_type": "markdown",
      "source": [
        "### GitHub\n",
        "\n",
        "您可以通过依次转到“文件”>“在 GitHub 中保存一份副本…”，保存一个 Colab 笔记本副本\n",
        "\n",
        "只需在 colab.research.google.com/github/ 后面加上路径，即可在 GitHub 上加载任何 .ipynb。例如，[colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/_index.ipynb](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/_index.ipynb) 将在 GitHub 上加载[此 .ipynb](https://github.com/tensorflow/models/blob/master/samples/core/get_started/_index.ipynb)。\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "yv2XIwi5hQ_g"
      },
      "cell_type": "markdown",
      "source": [
        "### 可视化"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "rYs5mx2JZkmy"
      },
      "cell_type": "markdown",
      "source": [
        "Colaboratory 包含很多已被广泛使用的库（例如 [matplotlib](https://matplotlib.org/)），因而能够简化数据的可视化过程。"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "PTHnDj4y57ln",
        "outputId": "3460cc84-faf8-4d8c-a4e6-96a6809c389a",
        "colab": {
          "height": 360
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = np.arange(20)\n",
        "y = [x_i + np.random.randn(1) for x_i in x]\n",
        "a, b = np.polyfit(x, y, 1)\n",
        "_ = plt.plot(x, y, 'o', np.arange(20), a*np.arange(20)+b, '-')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pbHNv4qHMpRqZLSIRSQUtpgoEA/zP99/ive5TGM4RbIFoHk15mm8/sh6nRmaL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\nRK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x562d64cf1b10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "AN_LRQ9NkOjs"
      },
      "cell_type": "markdown",
      "source": [
        "想使用新的库？请在笔记本的顶部通过 `pip install` 命令安装该库。然后，您就可以在笔记本的任何其他位置使用该库。要了解导入常用库的方法，请参阅[导入库示例笔记本](/notebooks/snippets/importing_libraries.ipynb)。"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "qGBvZs4T58jq",
        "outputId": "6e404831-3336-4633-b76f-c6313ecdd356",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install -q matplotlib-venn\n",
        "\n",
        "from matplotlib_venn import venn2\n",
        "_ = venn2(subsets = (3, 2, 1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HS+lkU9smrZbT34h+JQYAjz7KcxWpZsyyi7ZRA6bi307jyQSmURIdg6qkva4d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\nxIiISFn/H6CFJPx9gio9AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f48ff99ff10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "rTX3heEtu0b2"
      },
      "cell_type": "markdown",
      "source": [
        "### 本地运行时支持\n",
        "\n",
        "Colab 支持连接本地计算机上的 Jupyter 运行时。有关详情，请参阅我们的[文档](https://research.google.com/colaboratory/local-runtimes.html)。"
      ]
    }
  ]
}